Overview

Dataset statistics

Number of variables10
Number of observations2824
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory245.4 KiB
Average record size in memory89.0 B

Variable types

Numeric10

Alerts

frequency_days is highly overall correlated with monetary_per_dayHigh correlation
gross_revenue is highly overall correlated with lifetime_days and 3 other fieldsHigh correlation
lifetime_days is highly overall correlated with gross_revenue and 1 other fieldsHigh correlation
monetary_per_day is highly overall correlated with frequency_days and 2 other fieldsHigh correlation
orders_count is highly overall correlated with gross_revenue and 2 other fieldsHigh correlation
qtd_produtos is highly overall correlated with gross_revenue and 2 other fieldsHigh correlation
returns is highly overall correlated with returns_rateHigh correlation
returns_rate is highly overall correlated with returnsHigh correlation
returns is highly skewed (γ1 = 21.82144361)Skewed
monetary_per_day is highly skewed (γ1 = 45.64509447)Skewed
customer_id has unique valuesUnique
recency has 33 (1.2%) zerosZeros
frequency_days has 51 (1.8%) zerosZeros
returns has 1524 (54.0%) zerosZeros
returns_rate has 1524 (54.0%) zerosZeros

Reproduction

Analysis started2025-12-25 04:34:10.521122
Analysis finished2025-12-25 04:34:23.350508
Duration12.83 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

Unique 

Distinct2824
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15299.481
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.9 KiB
2025-12-25T01:34:23.455506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12626.15
Q113826.25
median15270.5
Q316801.75
95-th percentile17953.55
Maximum18287
Range5940
Interquartile range (IQR)2975.5

Descriptive statistics

Standard deviation1714.421
Coefficient of variation (CV)0.11205747
Kurtosis-1.2049882
Mean15299.481
Median Absolute Deviation (MAD)1484
Skewness0.0027127875
Sum43205733
Variance2939239.5
MonotonicityNot monotonic
2025-12-25T01:34:23.571511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178501
 
< 0.1%
130471
 
< 0.1%
125831
 
< 0.1%
137481
 
< 0.1%
151001
 
< 0.1%
152911
 
< 0.1%
146881
 
< 0.1%
178091
 
< 0.1%
153111
 
< 0.1%
160981
 
< 0.1%
Other values (2814)2814
99.6%
ValueCountFrequency (%)
123471
< 0.1%
123481
< 0.1%
123521
< 0.1%
123561
< 0.1%
123581
< 0.1%
123591
< 0.1%
123601
< 0.1%
123621
< 0.1%
123641
< 0.1%
123701
< 0.1%
ValueCountFrequency (%)
182871
< 0.1%
182831
< 0.1%
182821
< 0.1%
182731
< 0.1%
182721
< 0.1%
182701
< 0.1%
182651
< 0.1%
182631
< 0.1%
182611
< 0.1%
182601
< 0.1%

gross_revenue
Real number (ℝ)

High correlation 

Distinct2811
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2820.8528
Minimum36.56
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2025-12-25T01:34:23.669510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum36.56
5-th percentile251.876
Q1614.015
median1142.935
Q32388.8375
95-th percentile7433.5075
Maximum279138.02
Range279101.46
Interquartile range (IQR)1774.8225

Descriptive statistics

Standard deviation10401.624
Coefficient of variation (CV)3.687404
Kurtosis375.53948
Mean2820.8528
Median Absolute Deviation (MAD)683.02
Skewness17.129036
Sum7966088.4
Variance1.0819378 × 108
MonotonicityNot monotonic
2025-12-25T01:34:23.767506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2053.022
 
0.1%
1078.962
 
0.1%
379.652
 
0.1%
901.22
 
0.1%
598.22
 
0.1%
178.962
 
0.1%
3312
 
0.1%
1353.742
 
0.1%
745.062
 
0.1%
650.432
 
0.1%
Other values (2801)2804
99.3%
ValueCountFrequency (%)
36.561
< 0.1%
521
< 0.1%
52.21
< 0.1%
62.431
< 0.1%
68.841
< 0.1%
70.021
< 0.1%
77.41
< 0.1%
84.651
< 0.1%
90.31
< 0.1%
93.351
< 0.1%
ValueCountFrequency (%)
279138.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
140450.721
< 0.1%
124564.531
< 0.1%
117379.631
< 0.1%
91062.381
< 0.1%
72882.091
< 0.1%
66653.561
< 0.1%
65039.621
< 0.1%

recency
Real number (ℝ)

Zeros 

Distinct257
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.105524
Minimum0
Maximum373
Zeros33
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2025-12-25T01:34:23.886511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q110
median29
Q374
95-th percentile215
Maximum373
Range373
Interquartile range (IQR)64

Descriptive statistics

Standard deviation70.215004
Coefficient of variation (CV)1.208405
Kurtosis3.2614675
Mean58.105524
Median Absolute Deviation (MAD)24
Skewness1.8722169
Sum164090
Variance4930.1468
MonotonicityNot monotonic
2025-12-25T01:34:24.001508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199
 
3.5%
487
 
3.1%
286
 
3.0%
385
 
3.0%
876
 
2.7%
1069
 
2.4%
966
 
2.3%
765
 
2.3%
1762
 
2.2%
2256
 
2.0%
Other values (247)2073
73.4%
ValueCountFrequency (%)
033
 
1.2%
199
3.5%
286
3.0%
385
3.0%
487
3.1%
543
1.5%
765
2.3%
876
2.7%
966
2.3%
1069
2.4%
ValueCountFrequency (%)
3731
 
< 0.1%
3721
 
< 0.1%
3691
 
< 0.1%
3661
 
< 0.1%
3601
 
< 0.1%
3583
0.1%
3541
 
< 0.1%
3371
 
< 0.1%
3362
0.1%
3341
 
< 0.1%

qtd_produtos
Real number (ℝ)

High correlation 

Distinct1653
Distinct (%)58.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1648.7755
Minimum2
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2025-12-25T01:34:24.133510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile114.15
Q1323
median683.5
Q31468.25
95-th percentile4538.4
Maximum196844
Range196842
Interquartile range (IQR)1145.25

Descriptive statistics

Standard deviation5840.5383
Coefficient of variation (CV)3.5423491
Kurtosis493.5989
Mean1648.7755
Median Absolute Deviation (MAD)441.5
Skewness18.32383
Sum4656142
Variance34111888
MonotonicityNot monotonic
2025-12-25T01:34:24.273667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31011
 
0.4%
2468
 
0.3%
1508
 
0.3%
5167
 
0.2%
2197
 
0.2%
2007
 
0.2%
2607
 
0.2%
2727
 
0.2%
3947
 
0.2%
12007
 
0.2%
Other values (1643)2748
97.3%
ValueCountFrequency (%)
21
< 0.1%
161
< 0.1%
171
< 0.1%
191
< 0.1%
201
< 0.1%
241
< 0.1%
251
< 0.1%
272
0.1%
301
< 0.1%
321
< 0.1%
ValueCountFrequency (%)
1968441
< 0.1%
802631
< 0.1%
773731
< 0.1%
699931
< 0.1%
645491
< 0.1%
641241
< 0.1%
633121
< 0.1%
583431
< 0.1%
578851
< 0.1%
502551
< 0.1%

orders_count
Real number (ℝ)

High correlation 

Distinct55
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.983711
Minimum2
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2025-12-25T01:34:24.402509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median4
Q36
95-th percentile17
Maximum206
Range204
Interquartile range (IQR)4

Descriptive statistics

Standard deviation9.0058675
Coefficient of variation (CV)1.5050639
Kurtosis186.37707
Mean5.983711
Median Absolute Deviation (MAD)2
Skewness10.688153
Sum16898
Variance81.10565
MonotonicityNot monotonic
2025-12-25T01:34:24.622506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2825
29.2%
3503
17.8%
4394
14.0%
5237
 
8.4%
6173
 
6.1%
7138
 
4.9%
898
 
3.5%
969
 
2.4%
1055
 
1.9%
1154
 
1.9%
Other values (45)278
 
9.8%
ValueCountFrequency (%)
2825
29.2%
3503
17.8%
4394
14.0%
5237
 
8.4%
6173
 
6.1%
7138
 
4.9%
898
 
3.5%
969
 
2.4%
1055
 
1.9%
1154
 
1.9%
ValueCountFrequency (%)
2061
< 0.1%
1991
< 0.1%
1241
< 0.1%
971
< 0.1%
912
0.1%
861
< 0.1%
721
< 0.1%
622
0.1%
601
< 0.1%
571
< 0.1%

frequency_days
Real number (ℝ)

High correlation  Zeros 

Distinct1218
Distinct (%)43.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.078754
Minimum0
Maximum366
Zeros51
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2025-12-25T01:34:24.725505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.55
Q130
median54
Q392.666667
95-th percentile212.7
Maximum366
Range366
Interquartile range (IQR)62.666667

Descriptive statistics

Standard deviation65.524224
Coefficient of variation (CV)0.89662482
Kurtosis4.1469104
Mean73.078754
Median Absolute Deviation (MAD)28.703297
Skewness1.9133911
Sum206374.4
Variance4293.424
MonotonicityNot monotonic
2025-12-25T01:34:24.830507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
051
 
1.8%
3120
 
0.7%
7020
 
0.7%
1416
 
0.6%
2116
 
0.6%
4615
 
0.5%
4215
 
0.5%
5515
 
0.5%
4914
 
0.5%
2613
 
0.5%
Other values (1208)2629
93.1%
ValueCountFrequency (%)
051
1.8%
0.03030303031
 
< 0.1%
0.21
 
< 0.1%
0.33333333331
 
< 0.1%
0.85714285711
 
< 0.1%
18
 
0.3%
1.51
 
< 0.1%
1.8195121951
 
< 0.1%
1.8787878791
 
< 0.1%
23
 
0.1%
ValueCountFrequency (%)
3661
 
< 0.1%
3651
 
< 0.1%
3641
 
< 0.1%
3631
 
< 0.1%
3572
0.1%
3561
 
< 0.1%
3552
0.1%
3521
 
< 0.1%
3512
0.1%
3503
0.1%

returns
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct204
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.408994
Minimum0
Maximum9014
Zeros1524
Zeros (%)54.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2025-12-25T01:34:24.940507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38
95-th percentile93.7
Maximum9014
Range9014
Interquartile range (IQR)8

Descriptive statistics

Standard deviation288.11229
Coefficient of variation (CV)8.373168
Kurtosis582.17626
Mean34.408994
Median Absolute Deviation (MAD)0
Skewness21.821444
Sum97171
Variance83008.692
MonotonicityNot monotonic
2025-12-25T01:34:25.036507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01524
54.0%
1131
 
4.6%
2118
 
4.2%
382
 
2.9%
472
 
2.5%
663
 
2.2%
556
 
2.0%
1246
 
1.6%
839
 
1.4%
938
 
1.3%
Other values (194)655
23.2%
ValueCountFrequency (%)
01524
54.0%
1131
 
4.6%
2118
 
4.2%
382
 
2.9%
472
 
2.5%
556
 
2.0%
663
 
2.2%
738
 
1.3%
839
 
1.4%
938
 
1.3%
ValueCountFrequency (%)
90141
< 0.1%
80041
< 0.1%
44271
< 0.1%
37681
< 0.1%
33321
< 0.1%
28781
< 0.1%
20221
< 0.1%
20121
< 0.1%
17761
< 0.1%
15941
< 0.1%

returns_rate
Real number (ℝ)

High correlation  Zeros 

Distinct1243
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.017013814
Minimum0
Maximum1.5519126
Zeros1524
Zeros (%)54.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2025-12-25T01:34:25.133505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.0082890855
95-th percentile0.071953056
Maximum1.5519126
Range1.5519126
Interquartile range (IQR)0.0082890855

Descriptive statistics

Standard deviation0.068751358
Coefficient of variation (CV)4.040914
Kurtosis139.1544
Mean0.017013814
Median Absolute Deviation (MAD)0
Skewness9.6918246
Sum48.047011
Variance0.0047267493
MonotonicityNot monotonic
2025-12-25T01:34:25.233506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01524
54.0%
0.0096618357494
 
0.1%
0.53
 
0.1%
0.0074626865673
 
0.1%
0.02439024393
 
0.1%
0.013605442183
 
0.1%
0.014925373133
 
0.1%
0.0024630541873
 
0.1%
0.012295081973
 
0.1%
0.0023094688223
 
0.1%
Other values (1233)1272
45.0%
ValueCountFrequency (%)
01524
54.0%
0.00011696362431
 
< 0.1%
0.00018399264031
 
< 0.1%
0.00028169014081
 
< 0.1%
0.00031407035181
 
< 0.1%
0.00036192544341
 
< 0.1%
0.00036324010171
 
< 0.1%
0.00036376864311
 
< 0.1%
0.00036710719531
 
< 0.1%
0.0003930817611
 
< 0.1%
ValueCountFrequency (%)
1.5519125681
< 0.1%
11
< 0.1%
0.98630136991
< 0.1%
0.63333333331
< 0.1%
0.60088365241
< 0.1%
0.59645669291
< 0.1%
0.56488549621
< 0.1%
0.56463878331
< 0.1%
0.56020408161
< 0.1%
0.53990610331
< 0.1%

lifetime_days
Real number (ℝ)

High correlation 

Distinct374
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201.35517
Minimum1
Maximum374
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2025-12-25T01:34:25.331509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18
Q1102
median210
Q3302
95-th percentile363
Maximum374
Range373
Interquartile range (IQR)200

Descriptive statistics

Standard deviation113.49623
Coefficient of variation (CV)0.56366187
Kurtosis-1.225398
Mean201.35517
Median Absolute Deviation (MAD)99
Skewness-0.15790104
Sum568627
Variance12881.395
MonotonicityNot monotonic
2025-12-25T01:34:25.432506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
151
 
1.8%
36528
 
1.0%
35126
 
0.9%
35825
 
0.9%
36720
 
0.7%
35220
 
0.7%
35619
 
0.7%
34418
 
0.6%
36618
 
0.6%
18917
 
0.6%
Other values (364)2582
91.4%
ValueCountFrequency (%)
151
1.8%
29
 
0.3%
34
 
0.1%
45
 
0.2%
54
 
0.1%
63
 
0.1%
72
 
0.1%
87
 
0.2%
95
 
0.2%
103
 
0.1%
ValueCountFrequency (%)
3745
 
0.2%
3738
 
0.3%
3728
 
0.3%
3718
 
0.3%
3706
 
0.2%
36910
 
0.4%
36811
 
0.4%
36720
0.7%
36618
0.6%
36528
1.0%

monetary_per_day
Real number (ℝ)

High correlation  Skewed 

Distinct2822
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.616004
Minimum0.11078788
Maximum39916.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2025-12-25T01:34:25.914553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.11078788
5-th percentile1.6363994
Q14.0651576
median7.896662
Q315.630297
95-th percentile67.006128
Maximum39916.5
Range39916.389
Interquartile range (IQR)11.565139

Descriptive statistics

Standard deviation797.8526
Coefficient of variation (CV)17.490629
Kurtosis2231.5863
Mean45.616004
Median Absolute Deviation (MAD)4.6874122
Skewness45.645094
Sum128819.6
Variance636568.77
MonotonicityNot monotonic
2025-12-25T01:34:26.026550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.7563636362
 
0.1%
26.42
 
0.1%
18.025215051
 
< 0.1%
3.398745521
 
< 0.1%
21.365853661
 
< 0.1%
13.247277941
 
< 0.1%
90.127142861
 
< 0.1%
43.279237541
 
< 0.1%
2695.6051
 
< 0.1%
80.130434781
 
< 0.1%
Other values (2812)2812
99.6%
ValueCountFrequency (%)
0.11078787881
< 0.1%
0.14262295081
< 0.1%
0.33502392341
< 0.1%
0.37127192981
< 0.1%
0.40976190481
< 0.1%
0.48557692311
< 0.1%
0.59024390241
< 0.1%
0.61153846151
< 0.1%
0.62457478011
< 0.1%
0.63645569621
< 0.1%
ValueCountFrequency (%)
39916.51
< 0.1%
12393.71
< 0.1%
4219.171
< 0.1%
2949.751
< 0.1%
2695.6051
< 0.1%
1760.961
< 0.1%
1345.621
< 0.1%
1309.141
< 0.1%
1145.61
< 0.1%
977.771
< 0.1%

Interactions

2025-12-25T01:34:21.799281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:10.935101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:11.947104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:13.472123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:14.597119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:15.940395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:17.158962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:18.192960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:19.559672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:20.659356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:21.899279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:11.072111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:12.083108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:13.572122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:14.730117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:16.053343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:17.260966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:18.284955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:19.659435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:20.769870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:22.015283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:11.167106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:12.185108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:13.675109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:14.862181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:16.169631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:17.375961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:18.379961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:19.782871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:20.881248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:22.144279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:11.261109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:12.276109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:13.790104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:14.997104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:16.285501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:17.479970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:18.471967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:19.883140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:20.981698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:22.322289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:11.365104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:12.380112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:13.912107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:15.141559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:16.401510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:17.590974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:18.563962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:19.984444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:21.095883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:22.534281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:11.463103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:12.475101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:14.019108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:15.299768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:16.525829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:17.693973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:18.671968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:20.099381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:21.215879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:22.643279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:11.558109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:13.044103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:14.138110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:15.451894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:16.639579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:17.787956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:18.764959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:20.206050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:21.329384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:22.756281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:11.653104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:13.143109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:14.244107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:15.556136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:16.750076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:17.885962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:18.846957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:20.317502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:21.446277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:22.864275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:11.749107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:13.250113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:14.354107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:15.688427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:16.863626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:17.985975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:19.363677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:20.426205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:21.563280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:22.968280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:11.847112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:13.365107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:14.476113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:15.809308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:16.986180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:18.086955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:19.455674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:20.543085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-25T01:34:21.661276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-25T01:34:26.108550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
customer_idfrequency_daysgross_revenuelifetime_daysmonetary_per_dayorders_countqtd_produtosrecencyreturnsreturns_rate
customer_id1.000-0.032-0.094-0.009-0.0700.004-0.0850.014-0.062-0.052
frequency_days-0.0321.000-0.3430.231-0.763-0.453-0.3180.188-0.216-0.147
gross_revenue-0.094-0.3431.0000.5230.5610.7640.920-0.3790.4630.332
lifetime_days-0.0090.2310.5231.000-0.2700.6530.485-0.4350.3050.226
monetary_per_day-0.070-0.7630.561-0.2701.0000.2440.522-0.1140.2520.160
orders_count0.004-0.4530.7640.6530.2441.0000.705-0.4530.4310.323
qtd_produtos-0.085-0.3180.9200.4850.5220.7051.000-0.3720.4250.281
recency0.0140.188-0.379-0.435-0.114-0.453-0.3721.000-0.189-0.122
returns-0.062-0.2160.4630.3050.2520.4310.425-0.1891.0000.970
returns_rate-0.052-0.1470.3320.2260.1600.3230.281-0.1220.9701.000

Missing values

2025-12-25T01:34:23.134506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-25T01:34:23.255517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idgross_revenuerecencyqtd_produtosorders_countfrequency_daysreturnsreturns_ratelifetime_daysmonetary_per_day
0178505391.21372.01733.034.00.03030340.00.0230812.02695.605000
1130473232.5956.01390.09.039.62500035.00.025180318.010.165377
2125836705.382.05028.015.026.50000050.00.009944372.018.025215
313748948.2595.0439.05.069.5000000.00.000000279.03.398746
415100876.00333.080.03.020.00000022.00.27500041.021.365854
5152914623.3025.02102.014.026.76923129.00.013796349.013.247278
6146885630.877.03621.021.018.300000399.00.110191367.015.342970
7178095411.9116.02057.012.032.45454541.00.019932358.015.117067
81531160767.900.038194.091.04.144444474.00.012410374.0162.481016
9160982005.6387.0613.07.047.6666670.00.000000287.06.988258
customer_idgross_revenuerecencyqtd_produtosorders_countfrequency_daysreturnsreturns_ratelifetime_daysmonetary_per_day
563717468137.0010.0116.02.04.0000000.00.0000005.027.400000
564813596697.045.0406.02.07.0000000.00.0000008.087.130000
5654148931237.859.0799.02.02.0000000.00.0000003.0412.616667
565617852114.3411.053.02.00.0000000.00.0000001.0114.340000
567317772182.7710.058.02.00.0000000.00.0000001.0182.770000
567914126706.137.0508.03.01.50000050.00.0984254.0176.532500
568016479300.8310.0102.02.00.0000000.00.0000001.0300.830000
5685135211092.391.0733.03.04.5000000.00.00000010.0109.239000
569515060301.848.0262.04.00.3333330.00.0000002.0150.920000
57651600012393.702.05110.03.00.0000000.00.0000001.012393.700000